![](bg8.png)
factors that should be included in the distribution of
sensor nodes in the urban environment. Are these
factors enough for the effective distribution of
sensor nodes in the urban environment or are there
other significant influences? Some factors can be
more important than the others in the different types
of application. Should the suggested factors be
evaluated by weights? The graph theory is a way
how to express the communication in the wireless
sensor network. Seven types of graphs are usually
used in the wireless sensor networks. Would it be
possible to use some other type of graph that is not
primarily used in the wireless sensor networks or
suggest the new one? The graph theory is not the
only method that can solve the problem of
distribution of wireless sensor nodes in the urban
environment. There are other methods like chaos
theory that can be used for the distribution of sensor
nodes. Would this theory be more appropriate for
implementing terrain characteristics in the
calculation? The problem of implementing terrain
factors into the distribution methods is crucial in the
case that the sensor nodes are situated in the urban
environment. The solution of this problem would
make the distribution of sensor nodes more efficient.
REFERENCES
Baranidharan B., Santhi B., 2012. EEGTP: Energy
Efficient Graph Theory Protocol for Wireless Sensor
Networks. Information Technology Journal, 11, 808-
811.
Kawahigashi, H., 2005. Modeling Ad hoc Sensor
Networks using Random Graph Theory. IEEE . Japan.
http://jan.newmarch.name/conferences/ccnc05/DATA/
1-N02-04.PDF
Mizera, J., 2011. Využití senzorových bezdrátových sítí
pro monitorování životního prostředí. Brno. Bachelor
Thesis. VUT Brno. http://www.vutbr.cz/www_base/za
v_prace_soubor_verejne.php?file_id=42512.
Sarioz, D., 2012. Geometric graph theory and wireless
sensor networks. Dissertation thesis. University of
New York, New York, USA. http://gradworks.umi.co
m/3499283.pdf.
Ding, L., Guan Z., H., 2008. Modeling wireless sensor
networks using random graph theory. Physica,
vol. 1. http://www.researchgate.net/publication/22892
7499_Modeling_wireless_sensor_networks_using_ran
dom_graph_theory
Jorio, A., Elfkihi, S., Elbhiri, B., Aboutajdine, D., 2013. A
new clustering algorithm in WSN based on spectral
clustering and residual energy. Maroko
http://www.thinkmind.org/index.php?view=article&art
icleid=sensorcomm_2013_5_10_10186
Yan, X., Xi, J., Chicharo, Y., U., Y., 2008. An energy-
aware multilevel clustering algorithm for wireless
sensor networks. International Conference on
Intelligent Sensors, Sensor Networks and Information
Processing. Sydney, Australia.
Silva, A., Reynes, B., Debbah, M., 2009. Congestion in
Randomly Deployed Wireless Ad-Hoc and Sensor
Networks. Proc. Of International Conference on Ultra
Modern Telecommunications, St. Petersburg, Russia,
October 12-14.
Lu, G., Sadagopan, N., Krishnamachari, B., Goel, A.,
2005. Delay Efficient Sleep Scheduling in Wireless
Sensor Networks. USA. http://anrg.usc.edu/www/pape
rs/LuSadagopanKrishnamachariGoel_Infocom05.pdf
Haghpanahi, M., Kalantari, M., Shayman, M., 2012.
Topology control in large-scale wireless sensor
networks: Between information source and sink. Ad
Hoc Networks, vol. 11. http://www.sciencedirect.com/
science/article/pii/S1570870512002120#
Kar, S., Moura J., M., F., 2008. Sensor Networks With
Random Links: Topology Design for Distributed
Consensus. IEEE TRANSACTIONS ON SIGNAL
PROCESSING , vol. 56. http://users.ece.cmu.edu/~mo
ura/papers/t-sp-jul08-karmoura-ieeexplore.pdf
Stojmenović, I., 2005. Handbook of sensor networks:
algorithms and architectures. Hoboken, NJ: Wiley.
ISBN 0471684724.
Matula, D., V., Sokal, R., R., 2010. Properties of Gabriel
Graphs Relevant to Geographic Variation Research
and the Clustering of Points in the Plane. Geographical
Analysis. vol. 12. http://onlinelibrary.wiley.com/doi/1
0.1111/j.1538-4632.1980.tb00031.x/pdf
Scheideler, Ch., 2004. Overlay Networks for Wireless
Systems. In: Theory of Network Communication.
http://www.cs.jhu.edu/~scheideler/courses/600.348_F
04/lecture_13.pdf
SENSORNETS2014-DoctoralConsortium
10